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  <h1>Source code for torchvision.models.densenet</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">re</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
<span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>
<span class="kn">import</span> <span class="nn">torch.utils.checkpoint</span> <span class="k">as</span> <span class="nn">cp</span>
<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">OrderedDict</span>
<span class="kn">from</span> <span class="nn">.utils</span> <span class="kn">import</span> <span class="n">load_state_dict_from_url</span>
<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">Tensor</span>
<span class="kn">from</span> <span class="nn">torch.jit.annotations</span> <span class="kn">import</span> <span class="n">List</span>


<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;DenseNet&#39;</span><span class="p">,</span> <span class="s1">&#39;densenet121&#39;</span><span class="p">,</span> <span class="s1">&#39;densenet169&#39;</span><span class="p">,</span> <span class="s1">&#39;densenet201&#39;</span><span class="p">,</span> <span class="s1">&#39;densenet161&#39;</span><span class="p">]</span>

<span class="n">model_urls</span> <span class="o">=</span> <span class="p">{</span>
    <span class="s1">&#39;densenet121&#39;</span><span class="p">:</span> <span class="s1">&#39;https://download.pytorch.org/models/densenet121-a639ec97.pth&#39;</span><span class="p">,</span>
    <span class="s1">&#39;densenet169&#39;</span><span class="p">:</span> <span class="s1">&#39;https://download.pytorch.org/models/densenet169-b2777c0a.pth&#39;</span><span class="p">,</span>
    <span class="s1">&#39;densenet201&#39;</span><span class="p">:</span> <span class="s1">&#39;https://download.pytorch.org/models/densenet201-c1103571.pth&#39;</span><span class="p">,</span>
    <span class="s1">&#39;densenet161&#39;</span><span class="p">:</span> <span class="s1">&#39;https://download.pytorch.org/models/densenet161-8d451a50.pth&#39;</span><span class="p">,</span>
<span class="p">}</span>


<span class="k">class</span> <span class="nc">_DenseLayer</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_input_features</span><span class="p">,</span> <span class="n">growth_rate</span><span class="p">,</span> <span class="n">bn_size</span><span class="p">,</span> <span class="n">drop_rate</span><span class="p">,</span> <span class="n">memory_efficient</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">_DenseLayer</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;norm1&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">num_input_features</span><span class="p">)),</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;relu1&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)),</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;conv1&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">num_input_features</span><span class="p">,</span> <span class="n">bn_size</span> <span class="o">*</span>
                                           <span class="n">growth_rate</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
                                           <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)),</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;norm2&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">bn_size</span> <span class="o">*</span> <span class="n">growth_rate</span><span class="p">)),</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;relu2&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)),</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;conv2&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">bn_size</span> <span class="o">*</span> <span class="n">growth_rate</span><span class="p">,</span> <span class="n">growth_rate</span><span class="p">,</span>
                                           <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
                                           <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)),</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">drop_rate</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">drop_rate</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">memory_efficient</span> <span class="o">=</span> <span class="n">memory_efficient</span>

    <span class="k">def</span> <span class="nf">bn_function</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inputs</span><span class="p">):</span>
        <span class="c1"># type: (List[Tensor]) -&gt; Tensor</span>
        <span class="n">concated_features</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
        <span class="n">bottleneck_output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">relu1</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">norm1</span><span class="p">(</span><span class="n">concated_features</span><span class="p">)))</span>  <span class="c1"># noqa: T484</span>
        <span class="k">return</span> <span class="n">bottleneck_output</span>

    <span class="c1"># todo: rewrite when torchscript supports any</span>
    <span class="k">def</span> <span class="nf">any_requires_grad</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
        <span class="c1"># type: (List[Tensor]) -&gt; bool</span>
        <span class="k">for</span> <span class="n">tensor</span> <span class="ow">in</span> <span class="nb">input</span><span class="p">:</span>
            <span class="k">if</span> <span class="n">tensor</span><span class="o">.</span><span class="n">requires_grad</span><span class="p">:</span>
                <span class="k">return</span> <span class="kc">True</span>
        <span class="k">return</span> <span class="kc">False</span>

    <span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">unused</span>  <span class="c1"># noqa: T484</span>
    <span class="k">def</span> <span class="nf">call_checkpoint_bottleneck</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
        <span class="c1"># type: (List[Tensor]) -&gt; Tensor</span>
        <span class="k">def</span> <span class="nf">closure</span><span class="p">(</span><span class="o">*</span><span class="n">inputs</span><span class="p">):</span>
            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">bn_function</span><span class="p">(</span><span class="o">*</span><span class="n">inputs</span><span class="p">)</span>

        <span class="k">return</span> <span class="n">cp</span><span class="o">.</span><span class="n">checkpoint</span><span class="p">(</span><span class="n">closure</span><span class="p">,</span> <span class="nb">input</span><span class="p">)</span>

    <span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">_overload_method</span>  <span class="c1"># noqa: F811</span>
    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
        <span class="c1"># type: (List[Tensor]) -&gt; (Tensor)</span>
        <span class="k">pass</span>

    <span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">_overload_method</span>  <span class="c1"># noqa: F811</span>
    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
        <span class="c1"># type: (Tensor) -&gt; (Tensor)</span>
        <span class="k">pass</span>

    <span class="c1"># torchscript does not yet support *args, so we overload method</span>
    <span class="c1"># allowing it to take either a List[Tensor] or single Tensor</span>
    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>  <span class="c1"># noqa: F811</span>
        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">Tensor</span><span class="p">):</span>
            <span class="n">prev_features</span> <span class="o">=</span> <span class="p">[</span><span class="nb">input</span><span class="p">]</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">prev_features</span> <span class="o">=</span> <span class="nb">input</span>

        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">memory_efficient</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">any_requires_grad</span><span class="p">(</span><span class="n">prev_features</span><span class="p">):</span>
            <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">is_scripting</span><span class="p">():</span>
                <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;Memory Efficient not supported in JIT&quot;</span><span class="p">)</span>

            <span class="n">bottleneck_output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">call_checkpoint_bottleneck</span><span class="p">(</span><span class="n">prev_features</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">bottleneck_output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bn_function</span><span class="p">(</span><span class="n">prev_features</span><span class="p">)</span>

        <span class="n">new_features</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">relu2</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">norm2</span><span class="p">(</span><span class="n">bottleneck_output</span><span class="p">)))</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">drop_rate</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
            <span class="n">new_features</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">new_features</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">drop_rate</span><span class="p">,</span>
                                     <span class="n">training</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">training</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">new_features</span>


<span class="k">class</span> <span class="nc">_DenseBlock</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">ModuleDict</span><span class="p">):</span>
    <span class="n">_version</span> <span class="o">=</span> <span class="mi">2</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_layers</span><span class="p">,</span> <span class="n">num_input_features</span><span class="p">,</span> <span class="n">bn_size</span><span class="p">,</span> <span class="n">growth_rate</span><span class="p">,</span> <span class="n">drop_rate</span><span class="p">,</span> <span class="n">memory_efficient</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">_DenseBlock</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_layers</span><span class="p">):</span>
            <span class="n">layer</span> <span class="o">=</span> <span class="n">_DenseLayer</span><span class="p">(</span>
                <span class="n">num_input_features</span> <span class="o">+</span> <span class="n">i</span> <span class="o">*</span> <span class="n">growth_rate</span><span class="p">,</span>
                <span class="n">growth_rate</span><span class="o">=</span><span class="n">growth_rate</span><span class="p">,</span>
                <span class="n">bn_size</span><span class="o">=</span><span class="n">bn_size</span><span class="p">,</span>
                <span class="n">drop_rate</span><span class="o">=</span><span class="n">drop_rate</span><span class="p">,</span>
                <span class="n">memory_efficient</span><span class="o">=</span><span class="n">memory_efficient</span><span class="p">,</span>
            <span class="p">)</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;denselayer</span><span class="si">%d</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span> <span class="n">layer</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">init_features</span><span class="p">):</span>
        <span class="n">features</span> <span class="o">=</span> <span class="p">[</span><span class="n">init_features</span><span class="p">]</span>
        <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">layer</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
            <span class="n">new_features</span> <span class="o">=</span> <span class="n">layer</span><span class="p">(</span><span class="n">features</span><span class="p">)</span>
            <span class="n">features</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">new_features</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">features</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>


<span class="k">class</span> <span class="nc">_Transition</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">):</span>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_input_features</span><span class="p">,</span> <span class="n">num_output_features</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">_Transition</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;norm&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">num_input_features</span><span class="p">))</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;conv&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">num_input_features</span><span class="p">,</span> <span class="n">num_output_features</span><span class="p">,</span>
                                          <span class="n">kernel_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">))</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;pool&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">AvgPool2d</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>


<span class="k">class</span> <span class="nc">DenseNet</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Densenet-BC model class, based on</span>
<span class="sd">    `&quot;Densely Connected Convolutional Networks&quot; &lt;https://arxiv.org/pdf/1608.06993.pdf&gt;`_</span>

<span class="sd">    Args:</span>
<span class="sd">        growth_rate (int) - how many filters to add each layer (`k` in paper)</span>
<span class="sd">        block_config (list of 4 ints) - how many layers in each pooling block</span>
<span class="sd">        num_init_features (int) - the number of filters to learn in the first convolution layer</span>
<span class="sd">        bn_size (int) - multiplicative factor for number of bottle neck layers</span>
<span class="sd">          (i.e. bn_size * k features in the bottleneck layer)</span>
<span class="sd">        drop_rate (float) - dropout rate after each dense layer</span>
<span class="sd">        num_classes (int) - number of classification classes</span>
<span class="sd">        memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,</span>
<span class="sd">          but slower. Default: *False*. See `&quot;paper&quot; &lt;https://arxiv.org/pdf/1707.06990.pdf&gt;`_</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">growth_rate</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">block_config</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="mi">24</span><span class="p">,</span> <span class="mi">16</span><span class="p">),</span>
                 <span class="n">num_init_features</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <span class="n">bn_size</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">drop_rate</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">num_classes</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">memory_efficient</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>

        <span class="nb">super</span><span class="p">(</span><span class="n">DenseNet</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>

        <span class="c1"># First convolution</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">features</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="n">OrderedDict</span><span class="p">([</span>
            <span class="p">(</span><span class="s1">&#39;conv0&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">num_init_features</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
                                <span class="n">padding</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)),</span>
            <span class="p">(</span><span class="s1">&#39;norm0&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">num_init_features</span><span class="p">)),</span>
            <span class="p">(</span><span class="s1">&#39;relu0&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)),</span>
            <span class="p">(</span><span class="s1">&#39;pool0&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">)),</span>
        <span class="p">]))</span>

        <span class="c1"># Each denseblock</span>
        <span class="n">num_features</span> <span class="o">=</span> <span class="n">num_init_features</span>
        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">num_layers</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">block_config</span><span class="p">):</span>
            <span class="n">block</span> <span class="o">=</span> <span class="n">_DenseBlock</span><span class="p">(</span>
                <span class="n">num_layers</span><span class="o">=</span><span class="n">num_layers</span><span class="p">,</span>
                <span class="n">num_input_features</span><span class="o">=</span><span class="n">num_features</span><span class="p">,</span>
                <span class="n">bn_size</span><span class="o">=</span><span class="n">bn_size</span><span class="p">,</span>
                <span class="n">growth_rate</span><span class="o">=</span><span class="n">growth_rate</span><span class="p">,</span>
                <span class="n">drop_rate</span><span class="o">=</span><span class="n">drop_rate</span><span class="p">,</span>
                <span class="n">memory_efficient</span><span class="o">=</span><span class="n">memory_efficient</span>
            <span class="p">)</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">features</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;denseblock</span><span class="si">%d</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span> <span class="n">block</span><span class="p">)</span>
            <span class="n">num_features</span> <span class="o">=</span> <span class="n">num_features</span> <span class="o">+</span> <span class="n">num_layers</span> <span class="o">*</span> <span class="n">growth_rate</span>
            <span class="k">if</span> <span class="n">i</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">block_config</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
                <span class="n">trans</span> <span class="o">=</span> <span class="n">_Transition</span><span class="p">(</span><span class="n">num_input_features</span><span class="o">=</span><span class="n">num_features</span><span class="p">,</span>
                                    <span class="n">num_output_features</span><span class="o">=</span><span class="n">num_features</span> <span class="o">//</span> <span class="mi">2</span><span class="p">)</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">features</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;transition</span><span class="si">%d</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span> <span class="n">trans</span><span class="p">)</span>
                <span class="n">num_features</span> <span class="o">=</span> <span class="n">num_features</span> <span class="o">//</span> <span class="mi">2</span>

        <span class="c1"># Final batch norm</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">features</span><span class="o">.</span><span class="n">add_module</span><span class="p">(</span><span class="s1">&#39;norm5&#39;</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="n">num_features</span><span class="p">))</span>

        <span class="c1"># Linear layer</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">num_features</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">)</span>

        <span class="c1"># Official init from torch repo.</span>
        <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">modules</span><span class="p">():</span>
            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">):</span>
                <span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">kaiming_normal_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">weight</span><span class="p">)</span>
            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">):</span>
                <span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
                <span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">bias</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">):</span>
                <span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">constant_</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">bias</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
        <span class="n">features</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">features</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">out</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">features</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
        <span class="n">out</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">adaptive_avg_pool2d</span><span class="p">(</span><span class="n">out</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
        <span class="n">out</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">out</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">out</span>


<span class="k">def</span> <span class="nf">_load_state_dict</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">model_url</span><span class="p">,</span> <span class="n">progress</span><span class="p">):</span>
    <span class="c1"># &#39;.&#39;s are no longer allowed in module names, but previous _DenseLayer</span>
    <span class="c1"># has keys &#39;norm.1&#39;, &#39;relu.1&#39;, &#39;conv.1&#39;, &#39;norm.2&#39;, &#39;relu.2&#39;, &#39;conv.2&#39;.</span>
    <span class="c1"># They are also in the checkpoints in model_urls. This pattern is used</span>
    <span class="c1"># to find such keys.</span>
    <span class="n">pattern</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span>
        <span class="sa">r</span><span class="s1">&#39;^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$&#39;</span><span class="p">)</span>

    <span class="n">state_dict</span> <span class="o">=</span> <span class="n">load_state_dict_from_url</span><span class="p">(</span><span class="n">model_url</span><span class="p">,</span> <span class="n">progress</span><span class="o">=</span><span class="n">progress</span><span class="p">)</span>
    <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">state_dict</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
        <span class="n">res</span> <span class="o">=</span> <span class="n">pattern</span><span class="o">.</span><span class="n">match</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
        <span class="k">if</span> <span class="n">res</span><span class="p">:</span>
            <span class="n">new_key</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">group</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="n">res</span><span class="o">.</span><span class="n">group</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
            <span class="n">state_dict</span><span class="p">[</span><span class="n">new_key</span><span class="p">]</span> <span class="o">=</span> <span class="n">state_dict</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
            <span class="k">del</span> <span class="n">state_dict</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
    <span class="n">model</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">state_dict</span><span class="p">)</span>


<span class="k">def</span> <span class="nf">_densenet</span><span class="p">(</span><span class="n">arch</span><span class="p">,</span> <span class="n">growth_rate</span><span class="p">,</span> <span class="n">block_config</span><span class="p">,</span> <span class="n">num_init_features</span><span class="p">,</span> <span class="n">pretrained</span><span class="p">,</span> <span class="n">progress</span><span class="p">,</span>
              <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
    <span class="n">model</span> <span class="o">=</span> <span class="n">DenseNet</span><span class="p">(</span><span class="n">growth_rate</span><span class="p">,</span> <span class="n">block_config</span><span class="p">,</span> <span class="n">num_init_features</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">pretrained</span><span class="p">:</span>
        <span class="n">_load_state_dict</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">model_urls</span><span class="p">[</span><span class="n">arch</span><span class="p">],</span> <span class="n">progress</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">model</span>


<div class="viewcode-block" id="densenet121"><a class="viewcode-back" href="../../../models.html#torchvision.models.densenet121">[docs]</a><span class="k">def</span> <span class="nf">densenet121</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">progress</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Densenet-121 model from</span>
<span class="sd">    `&quot;Densely Connected Convolutional Networks&quot; &lt;https://arxiv.org/pdf/1608.06993.pdf&gt;`_</span>

<span class="sd">    Args:</span>
<span class="sd">        pretrained (bool): If True, returns a model pre-trained on ImageNet</span>
<span class="sd">        progress (bool): If True, displays a progress bar of the download to stderr</span>
<span class="sd">        memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,</span>
<span class="sd">          but slower. Default: *False*. See `&quot;paper&quot; &lt;https://arxiv.org/pdf/1707.06990.pdf&gt;`_</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">return</span> <span class="n">_densenet</span><span class="p">(</span><span class="s1">&#39;densenet121&#39;</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="mi">24</span><span class="p">,</span> <span class="mi">16</span><span class="p">),</span> <span class="mi">64</span><span class="p">,</span> <span class="n">pretrained</span><span class="p">,</span> <span class="n">progress</span><span class="p">,</span>
                     <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>


<div class="viewcode-block" id="densenet161"><a class="viewcode-back" href="../../../models.html#torchvision.models.densenet161">[docs]</a><span class="k">def</span> <span class="nf">densenet161</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">progress</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Densenet-161 model from</span>
<span class="sd">    `&quot;Densely Connected Convolutional Networks&quot; &lt;https://arxiv.org/pdf/1608.06993.pdf&gt;`_</span>

<span class="sd">    Args:</span>
<span class="sd">        pretrained (bool): If True, returns a model pre-trained on ImageNet</span>
<span class="sd">        progress (bool): If True, displays a progress bar of the download to stderr</span>
<span class="sd">        memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,</span>
<span class="sd">          but slower. Default: *False*. See `&quot;paper&quot; &lt;https://arxiv.org/pdf/1707.06990.pdf&gt;`_</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">return</span> <span class="n">_densenet</span><span class="p">(</span><span class="s1">&#39;densenet161&#39;</span><span class="p">,</span> <span class="mi">48</span><span class="p">,</span> <span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="mi">36</span><span class="p">,</span> <span class="mi">24</span><span class="p">),</span> <span class="mi">96</span><span class="p">,</span> <span class="n">pretrained</span><span class="p">,</span> <span class="n">progress</span><span class="p">,</span>
                     <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>


<div class="viewcode-block" id="densenet169"><a class="viewcode-back" href="../../../models.html#torchvision.models.densenet169">[docs]</a><span class="k">def</span> <span class="nf">densenet169</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">progress</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Densenet-169 model from</span>
<span class="sd">    `&quot;Densely Connected Convolutional Networks&quot; &lt;https://arxiv.org/pdf/1608.06993.pdf&gt;`_</span>

<span class="sd">    Args:</span>
<span class="sd">        pretrained (bool): If True, returns a model pre-trained on ImageNet</span>
<span class="sd">        progress (bool): If True, displays a progress bar of the download to stderr</span>
<span class="sd">        memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,</span>
<span class="sd">          but slower. Default: *False*. See `&quot;paper&quot; &lt;https://arxiv.org/pdf/1707.06990.pdf&gt;`_</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">return</span> <span class="n">_densenet</span><span class="p">(</span><span class="s1">&#39;densenet169&#39;</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">),</span> <span class="mi">64</span><span class="p">,</span> <span class="n">pretrained</span><span class="p">,</span> <span class="n">progress</span><span class="p">,</span>
                     <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>


<div class="viewcode-block" id="densenet201"><a class="viewcode-back" href="../../../models.html#torchvision.models.densenet201">[docs]</a><span class="k">def</span> <span class="nf">densenet201</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">progress</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Densenet-201 model from</span>
<span class="sd">    `&quot;Densely Connected Convolutional Networks&quot; &lt;https://arxiv.org/pdf/1608.06993.pdf&gt;`_</span>

<span class="sd">    Args:</span>
<span class="sd">        pretrained (bool): If True, returns a model pre-trained on ImageNet</span>
<span class="sd">        progress (bool): If True, displays a progress bar of the download to stderr</span>
<span class="sd">        memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,</span>
<span class="sd">          but slower. Default: *False*. See `&quot;paper&quot; &lt;https://arxiv.org/pdf/1707.06990.pdf&gt;`_</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">return</span> <span class="n">_densenet</span><span class="p">(</span><span class="s1">&#39;densenet201&#39;</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">12</span><span class="p">,</span> <span class="mi">48</span><span class="p">,</span> <span class="mi">32</span><span class="p">),</span> <span class="mi">64</span><span class="p">,</span> <span class="n">pretrained</span><span class="p">,</span> <span class="n">progress</span><span class="p">,</span>
                     <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
</pre></div>

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